Bibliographic record
Abstract
absorptive capacity in Argentina 204-6 in Canada 56, 216 in Costa Rica 56, 229-31 GM crops 204-5 influences on 16-17 in Jamaica 56 in Malta 56 meaning 18 mobile phones 205-6, 231, 288 in Mozambique 287-9 open source software 205-6, 230-31, 288 plant tissue cultures 205, 230-31, 287 recombinant insulin 204, 230 technology-specific nature of 13-14 trends 56 in United States 56 Advisory National Commission of Agricultural Biotechnology (CONABIA)(Argentina) 209, 213 Alcatel-Lucent 260 América Móvil Claro 128, 206 Angel Gallardo 95 anti-trust policy, influences of 19, 325 Apache 162, 164-5, 260 Apple 129-30 Argentina economic and developmental background 26-9 economic indicators 24, 28 educational trends 28 emerging technologies, generally absorptive capacity 204-6 access and penetration rates 210-12 diffusion generally 212-13 inequalities 207-8 policy and regulation, influence of 206-10 socio-economic influences on 203 employment trends 106 equal access policies 193 gender equality trends 28 GM crops absorptive capacity 204-5 access and penetration rates 211 distributional benefits 109-11, 113 farm structure 104 global role in 13 industry and farm effects 109-11 industry employment trends 106 industry structure 101-2 policy and regulatory influences on 96, 207-9 healthcare access to 209-10 expenditure trends 69 health insurance 82 subsidies 82 human development trends 29 income equality trends 56 mobile phones absorptive capacity 205-6 market structure 134 penetration rates 136-7, 210-11 policy and regulatory influences on 206-8 pre-paid service trends 141 service provider trends 128 universal service funds 133, 211 open source software absorptive capacity 205-6 access and penetration rates 211-12 corporate contributors 155 policy and regulatory influences on 156, 207-8, 210 plant tissue cultures absorptive capacity 205 access and penetration rates 211 business characteristics 187, 211
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.663 | 0.388 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".